CEP991B section 001 – spring 2014 Michigan State University – College of Education Causal Inference

Tuesdays 9:10-1:00 452 Erickson

Ken Frank [email protected] 517-355-9567 Room 462 Erickson Hall East Lansing, MI 48824-1034 http://www.msu.edu/~kenfrank/

Course Overview:

There is currently great debate regarding the basis for causal inferences across the social sciences. Can we make causal inferences only from experiments? What about ethical or logistical limitations, or concerns that the experimental paradigm is artificial because of the necessity for extreme control over conditions? On the other hand, though observational studies are applied to natural conditions, can we rely on statistical control to make causal inferences? What about unmeasured, or unrecognized confounding factors? At what point does a statistical inference sustain a causal inference? Answers to these questions are more than merely academic and philosophical. For example they have immediate implications for policy-making regarding the implementation of innovations.

To address questions such as the above this course will explore causal inference from the perspectives of statistics and philosophy of science. We will begin with a comparison of causal inferences in the social sciences with those of the experimental sciences. Drawing on eclectic readings (Manski, Heckman, Rubin, Holland, Pearl, Shadish, Cook, Campbell Sobel, Dawid), we will use concepts such as the counterfactual, homogeneity of units and internal and external validity to describe causal inference. Furthermore, we will discuss statistical techniques such as propensity score matching and instrumental variables that might be used to improve the likelihood of valid inferences. Finally, we will use recent work to quantify how robust inferences are to potential threats the validity.

In the first half the course I will present methods including regression, propensity score matching, instrumental variables, regression discontinuity, random versus fixed effects, and sensitivity analysis. In the second half of the course we will turn to intensive projects or readings. Students will be required to present and provide an intensive critique of an article featuring complex issues of causal inference. For the final project students will need to develop work toward a publishable

1 paper, either in the form of new method, or existing method applied to data of the students’ interest. Students may work in groups, with indications for who had primary responsibility for what work.

Memo on substantive interest and causal inference challenges due Feb 5.

Course Syllabus (to be enhanced)

Weeks 1-2: Review of regression Introduction to the counterfactual Robustness of inferences and sensitivity analyses

Weeks 3-6 Techniques for making causal inferences Propensity Scores Regression Discontinuity Randomized Experiments

March 4 [break]

Student and outside presentations:

Background

Holland, Paul W. 1986. "Statistics and Causal Inference." Journal of the American Statistical Association 81:945_70. (25-40) Morgan, Stephen L and Winship, Christopher. 2007. Counterfactuals and Causal Inference. New York: Cambridge University Press. Murnane, R. and Willett, J. 2011. Methods Matter. Improving Causal Inference in Educational and Social Science Research. Oxford: Oxford University Press. Rubin, Donald B. 1974. "Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies." Journal of Educational Psychology 66(5):688_701. (11-24) Shadish, W. R., Cook, T. D., and Campbell, D. T. (2002). Experimental and Quais-Experimental Designs for Generalized Causal Inference. New York: Houghton Mifflin. Sobel, Michael 1995. “Causal Inferences in the Social and Behavioral Sciences.” In Handbook of Statistical Modeling for the Social and Behavioral Sciences. Gerhard Arminger, Clifford Clogg and Michael Sobel (eds). New York: Plenum Press. (43-80) Winship, Christopher and Stephen L. Morgan. 2000. "The Estimation of Causal Effects From Observational Data." Annual Review of Sociology 25:659-706. (125-172)

http://www.wjh.harvard.edu/~winship/cfa_papers/causalinference.pdf

2 On the Web • http://www.wjh.harvard.edu/soc/faculty/winship/CFA_site.html (Winship’s portal) • http://www.ets.org/research/dload/AERA_2004-Holland.pdf (recent Paul Holland) • http://bayes.cs.ucla.edu/jp_home.html (Judea Pearl) • http://plato.stanford.edu/entries/causation-counterfactual/ (philosophy of counterfactual) • http://sekhon.berkeley.edu/causalinf/causalinf.pdf syllabus on causal inference syllabi: http://www.icpsr.umich.edu/icpsrweb/sumprog/syllabi/91295 http://www.biostat.jhsph.edu/~cfrangak/biostat_causal/syllabus.pdf http://www.cs.stevens.edu/~skleinbe/teaching/CI12/index.html http://www.cgdev.org/content/publications/detail/1424344/ http://steinhardt.nyu.edu/scmsAdmin/uploads/002/553/E10.2012Sp09.pdf http://www.ics.uci.edu/~sternh/courses/265/

3 Readings: The Counterfactual and Causal Inference

Morgan & Winship chapters 1 & 2.

Rubin, Donald B. 1974. "Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies." Journal of Educational Psychology 66(5):688-701. (11-24).

Holland, Paul W. 1986. "Statistics and Causal Inference." Journal of the American Statistical Association 81:945-70. (25-40).

Philosophical Underpinnings of Causal Inference Sobel, Michael 1995. “Causal Inferences in the Social and Behavioral Sciences.” In Handbook of Statistical Modeling for the Social and Behavioral Sciences. Gerhard Arminger, Clifford Clogg and Michael Sobel (eds). New York: Plenum Press. (43-80). Abbott, Andrew. 1998. "The Causal Devolution." Sociological Methods & Research 27(2):148-81. (81-97). Salmon, Wesley C. 1994. "Causality Without Counterfactuals." Philosophy of Science 61:297-312. (98-106). Einhorn, Hillel J. and Robin M. Hogarth. 1986. "Judging Probable Cause." Psychological Review 99(1):3-19. (107-123).

Supplemental: Pratt, John W. and Robert Schlaifer. 1988. "On the Interpretation and Observation of Laws." Journal of Econometrics 39:23-52. Dawid, A.P. 2000. “Causal Inference without Counterfactuals.” Journal of the American Statistical Association 95: 407-448.

Propensity Score Matching

Morgan and Winship: chapter 4; Murnane & Willett, chapter 12. Morgan, Stephen L. and David J. Harding. 2006. "Matching Estimators of Causal Effects: Prospects and Pitfalls in Theory and Practice." Sociological Methods and Research 35:3-60. Morgan, Stephen L. 2001. "Counterfactuals, Causal Effect Heterogeneity, and the Catholic School Effect on Learning." Sociology of Education 74: 341-374. http://www.soc.cornell.edu/faculty/morgan.shtml Rosenbaum, Paul R. and Donald B. Rubin. 1983. "The Central Role of the Propensity Score in Observational Studies for Causal Effects." Biometrika 70(1):41-55. Angrist JD, Krueger AB Instrumental variables and the search for identification: From supply and demand to natural experiments Journal of Economic Perspectives 15 (4): 69-85. http://www.uibk.ac.at/~cb0189/dl/jep01fall/06_instrument.pdf Rajeev H. Dehejia and Sadek Wahba. 2002. Propensity Score-Matching Methods for Nonexperimental Causal Studies. The Review of Economics and Statistics, Vol. 84, No. 1 (Feb., 2002), pp. 151-161.

Special issues: 4 Robert M. Pruzek (2011): Introduction to the Special Issue on Propensity Score Methods in Behavioral Research, Multivariate Behavioral Research, 46:3, 389-398 To link to this article: http://dx.doi.org/10.1080/00273171.2011.576618

Review of Economics and Statistics, February 2004, vol. 86, no. 1;

Journal of Econometrics, March-April 2005, vol.125, no. 1–2.

Instrumental variables Morgan and Winship: chapter 7; Murnane & Willett, chapter 10 Bollen, Kenneth A. “Instrumental Variables in Sociology and the Social Sciences”. Annual Review of Sociology Vol. 38: 37-72

Regression Discontinuity

Shadish et al., Chapter 7. See Shadish ppt for class. Murnane & Willett, chapter 9.

Statistical Significance, Causal Inference, and Sensitivity Analysis

Rosenbaum, Paul R. 1986. "Dropping Out of High School in the United States: An Observational Study." Journal of Educational Statistics 11(3):207-24. (280-289). Frank, K. 2000. “Impact of a Confounding Variable on the Inference of a Regression Coefficient.” Sociological Methods and Research, 29(2), 147-194. (Will hand out in class). Frank, Maroulis, Duong and Kelcey. Under re-review. What would it take to Change an Inference?: Using Rubin’s Causal Model to Interpret the Robustness of Causal Inferences. Education, Evaluation & Policy Analysis. Wilkinson, L & Task Force on Statistical Inference (1999). Statistical Methods in Psychology Journals: Guidelines and Explanations. American Psychologist, 54(8):594–604.(337-347) http://www.apa.org/journals/amp/amp548594.html.

VII. Causal Inference and Path Analysis

Duncan, O. 1966. “Path Analysis: Sociological Examples” American Journal of Sociology, Vol 72 (1), 1-16. (349-365). JSTOR: http://er.lib.msu.edu/ Freedman, David. 1997. "From Association to Causation Via Regression." Advances in Applied Mathematics 18:59-110. (366-393) Supplemental: Judea Pearl:http://bayes.cs.ucla.edu/jp_home.html, including papers and a slide show (Reasoning with Cause and Effect)

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